A Hybrid CNN-GRU-LSTM Power System Short-Term Load Forecasting Model Incorporating Attention Mechanism
In the process of power system operation,accurate prediction of short-term power load is an important condition to ensure the safe and economic operation of power system.The traditional single load model cannot fully capture the changes and nonlinear relationships of complex systems,and the prediction accuracy is low.For this reason,a hybrid prediction model integrating convolutional neural network with attention mechanism,gated reeurrent urrit and long and short-term memory network is proposed.Using the extraction of multi-dimensional data features,the introduction of the attention mechanism enhances and the ability to model long-term dependencies in serial data processing,further improving the model prediction accuracy.Comparative analysis experiments are conducted with practical examples,and the results show that the prediction accuracy of the hybrid prediction model is greatly improved compared with that of,and and etc.,verifying its effectiveness and superiority.
attention mechanismshort-term load predictionconvolutional neural networklong and short-term memory networkgated recurrent unithybrid model
NETL
NSTL
万方数据
